Article
Computer Science, Information Systems
Moti Zwilling
Summary: This study highlights the challenges posed by data science to students in the social sciences and suggests that they lack the necessary skills to cope with these challenges. It recommends designing adequate academic programs to equip students with the skills for big data analysis.
JOURNAL OF COMPUTER INFORMATION SYSTEMS
(2023)
Review
Oncology
Shawn M. Sweeney, Hisham K. Hamadeh, Natalie Abrams, Stacey J. Adam, Sara Brenner, Dana E. Connors, Gerard J. Davis, Louis Fiore, Susan H. Gawel, Robert L. Grossman, Sean E. Hanlon, Karl Hsu, Gary J. Kelloff, Ilan R. Kirsch, Bill Louv, Deven McGraw, Frank Meng, Daniel Milgram, Robert S. Miller, Emily Morgan, Lata Mukundan, Thomas O'Brien, Paul Robbins, Eric H. Rubin, Wendy S. Rubinstein, Liz Salmi, Teilo Schaller, George Shi, Caroline C. Sigman, Sudhir Srivastava
Summary: Big data in healthcare, including electronic health records, medical imaging, genomic sequencing, payor records, and data from pharmaceutical research, wearables, and medical devices, can provide unprecedented understanding of diseases and their treatment. However, challenges such as interoperability, data quality, and data privacy need to be addressed to fully leverage the potential of big data in medicine. Organizations sharing data in a precompetitive manner, establishing agreements on data quality standards, and implementing universal and practical data privacy principles are key to realizing the benefits of big data in healthcare.
Review
Chemistry, Multidisciplinary
Nicolai Lehnert, Bradley W. Musselman, Lance C. Seefeldt
Summary: Understanding the complex chemistry of enzymes involved in the Nitrogen Cycle is crucial in limiting anthropogenic effects on the environment.
CHEMICAL SOCIETY REVIEWS
(2021)
Review
Computer Science, Artificial Intelligence
Namrata Bhattacharya, Colleen C. Nelson, Gaurav Ahuja, Debarka Sengupta
Summary: Single-cell omics technologies have led to the development of new computational tools for analyzing large volumes of data, providing critical insights into biological systems. Opportunities for computational breakthroughs exist to accelerate single-cell research and gain newer insights into cellular biology.
WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY
(2021)
Article
Business
David Bell, Mark Lycett, Alaa Marshan, Asmat Monaghan
Summary: This paper examines the challenges of leveraging big data in the humanitarian sector in support of UN Sustainable Development Goal 17 Partnerships for the Goals. It presents empirical work integrating eight heterogeneous datasets to provide evidence of the inherent challenge of complexity resulting from differing levels of data granularity, and identifies five propositions to guide future research.
JOURNAL OF BUSINESS RESEARCH
(2021)
Review
Immunology
Rokhaya Ba, Estelle Geffard, Venceslas Douillard, Francoise Simon, Laurent Mesnard, Nicolas Vince, Pierre-Antoine Gourraud, Sophie Limou
Summary: In the era of big data, the quantity of available data in both research and care is increasing, which presents challenges for patients, caregivers, and researchers. In the field of transplantation, omics studies have greatly impacted research and the understanding of transplant outcomes. Integrating omics data is challenging due to biases and errors, and normalization and imputation methods have been developed to address these issues. The transplantation field brings additional complexity to omics analysis, and new strategies such as combined risk scores are emerging to better understand graft mechanisms.
Editorial Material
Biotechnology & Applied Microbiology
Esra Busra Isik, Michelle D. Brazas, Russell Schwartz, Bruno Gaeta, Patricia M. Palagi, Celia W. G. van Gelder, Prashanth Suravajhala, Harpreet Singh, Sarah L. Morgan, Hilyatuz Zahroh, Maurice Ling, Venkata P. Satagopam, Annette McGrath, Kenta Nakai, Tin Wee Tan, Ge Gao, Nicola Mulder, Christian Schonbach, Yun Zheng, Javier De Las Rivas, Asif M. Khan
Summary: Given the growing demand for bioinformatics expertise in the life sciences, a collective effort is required to proactively evaluate and address the challenges of educating and training life scientists with the requisite skills and competencies.
NATURE BIOTECHNOLOGY
(2023)
Article
Information Science & Library Science
Thanos Papadopoulos, M. E. Balta
Summary: This article addresses the lack of research on the role of Big Data and Analytics in addressing the challenges and opportunities created by Climate Change for operations and supply chains.
INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT
(2022)
Article
Chemistry, Multidisciplinary
Mladen Amovic, Miro Govedarica, Aleksandra Radulovic, Ivana Jankovic
Summary: Smart cities leverage digital technologies to enhance geospatial data exchange, boost data utilization, and establish new services for sustainable development. The GAMINESS management system, based on big data modeling principles, efficiently stores and manages vast amounts of structured, semi-structured, and unstructured data in real time, improving system performance through the five V principles.
APPLIED SCIENCES-BASEL
(2021)
Article
Psychology, Mathematical
Paul A. Brown, Ricardo A. Anderson
Summary: The characteristics of big data pose challenges for data analysis, requiring the use of computational methodologies. Previous data analytics methodologies can be applied to other disciplines. This paper introduces the Big Data Quality & Statistical Assurance model for behavioral scientists, involving data preprocessing and statistical quality phases.
BEHAVIOR RESEARCH METHODS
(2023)
Article
Multidisciplinary Sciences
Huadong Guo, Dong Liang, Zhongchang Sun, Fang Chen, Xinyuan Wang, Junsheng Li, Li Zhu, Jinhu Bian, Yanqiang Wei, Lei Huang, Yu Chen, Dailiang Peng, Xiaosong Li, Shanlong Lu, Jie Liu, Zeeshan Shirazi
Summary: The United Nations 2030 Agenda for Sustainable Development provides an important framework for economic, social, and environmental action. China's investment and successful case studies in Big Earth Data have significant implications for achieving the SDGs.
Article
Computer Science, Interdisciplinary Applications
Yannis Markonis, Christoforos Pappas, Martin Hanel, Simon Michael Papalexiou
Summary: This study introduces a graphical method for synthesizing and comparing observational and modeled data across a range of spatiotemporal scales. Instead of focusing on specific scales, the method examines the statistical properties of the data as they change across the spatiotemporal continuum. This cross-scale framework aids in identifying discrepancies between different scales.
ENVIRONMENTAL MODELLING & SOFTWARE
(2021)
Article
Mathematics
Jason S. Byers, Jeff Gill
Summary: This article introduces a method called BRSS for solving the computational problem of estimating Bayesian kriging models with big data. The method is shown to produce consistent estimates from the Bayesian spatial model and reduce bootstrap sample effects from a full-information Bayesian model. Realistic examples are provided to illustrate the application of the method.
Article
Environmental Studies
Leonidas Liakos, Panos Panagos
Summary: This study tackles the issue of soil resources through the processing of big data and discusses the challenges involved. It utilizes advanced mapping methods to convey comprehensive information on soil and other relevant factors, providing valuable insights for policymakers.
Article
Computer Science, Information Systems
Ameema Zainab, Ali Ghrayeb, Dabeeruddin Syed, Haitham Abu-Rub, Shady S. Refaat, Othmane Bouhali
Summary: Smart grids are reshaping the electricity transmission and distribution system globally by combining digital information with electrical power grids. Efficiently managing data generated from the grid is essential for successful knowledge extraction from smart grid big data. Scientific advancements are increasingly data-driven, presenting an intriguing area of research for data scientists. This poses computational challenges in developing new storage methods and data processing technologies. The paper emphasizes on studying big data management and proposing a management process for handling data in the grid, while leveraging data management tools and techniques to understand data sources and types in the grid.
